Abstract

Biomedical color images play major role in medical diagnosis. Often a change of state is identified through minute variations in color at tiny parts. Fuzzy C-means (FCM) clustering is suitable for pixel classification to isolate those parts but its success is heavily dependent on the selection of seed clusters. This paper presents a simple but effective technique to generate seed clusters resembling the image features. The HSI color model is selected for near-zero correlation among components. The approach has been tested on several cell images having low contrast at adjacent parts. Results of segmentation show its effectiveness.